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+Zipline
+=======
+
+|Gitter|
+|version status|
+|downloads|
+|build status|
+|Coverage Status|
+|Code quality|
+
+Zipline is a Pythonic algorithmic trading library. The system is
+fundamentally event-driven and a close approximation of how live-trading
+systems operate.
+
+Zipline is currently used in production as the backtesting engine
+powering `Quantopian Inc. <https://www.quantopian.com>`__ -- a free,
+community-centered platform that allows development and real-time
+backtesting of trading algorithms in the web browser.
+
+`Join our
+community! <https://groups.google.com/forum/#!forum/zipline>`__
+
+Want to contribute? See our `open
+requests <https://github.com/quantopian/zipline/wiki/Contribution-Requests>`__
+and our `general
+guidelines <https://github.com/quantopian/zipline#contributions>`__
+below.
+
+Features
+========
+
+-  Ease of use: Zipline tries to get out of your way so that you can
+   focus on algorithm development. See below for a code example.
+
+-  Zipline comes "batteries included" as many common statistics like
+   moving average and linear regression can be readily accessed from
+   within a user-written algorithm.
+
+-  Input of historical data and output of performance statistics is
+   based on Pandas DataFrames to integrate nicely into the existing
+   Python eco-system.
+
+-  Statistic and machine learning libraries like matplotlib, scipy,
+   statsmodels, and sklearn support development, analysis and
+   visualization of state-of-the-art trading systems.
+
+Installation
+============
+
+The easiest way to install Zipline is via ``conda`` which comes as part
+of `Anaconda <http://continuum.io/downloads>`__ or can be installed via
+``pip install conda``.
+
+Once set up, you can install Zipline from our Quantopian channel:
+
+::
+
+    conda install -c Quantopian zipline
+
+Currently supported platforms include:
+
+-  Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda)
+
+-  OSX 64-bit
+
+-  Linux 64-bit
+
+PIP
+---
+
+Alternatively you can install Zipline via the more traditional ``pip``
+command. Since zipline is pure-python code it should be very easy to
+install and set up:
+
+::
+
+    pip install numpy   # Pre-install numpy to handle dependency chain quirk
+    pip install zipline
+
+If there are problems installing the dependencies or zipline we
+recommend installing these packages via some other means. For Windows,
+the `Enthought Python
+Distribution <http://www.enthought.com/products/epd.php>`__ includes
+most of the necessary dependencies. On OSX, the `Scipy
+Superpack <http://fonnesbeck.github.com/ScipySuperpack/>`__ works very
+well.
+
+Dependencies
+------------
+
+-  Python (2.7 or 3.3)
+-  numpy (>= 1.6.0)
+-  pandas (>= 0.9.0)
+-  pytz
+-  Logbook
+-  requests
+-  `python-dateutil <https://pypi.python.org/pypi/python-dateutil>`__
+   (>= 2.1)
+-  ta-lib
+
+Quickstart
+==========
+
+See our `getting started
+tutorial <http://www.zipline.io/#quickstart>`__.
+
+The following code implements a simple dual moving average algorithm.
+
+.. code:: python
+
+    from zipline.api import order_target, record, symbol, history, add_history
+
+
+    def initialize(context):
+        # Register 2 histories that track daily prices,
+        # one with a 100 window and one with a 300 day window
+        add_history(100, '1d', 'price')
+        add_history(300, '1d', 'price')
+
+        context.i = 0
+
+
+    def handle_data(context, data):
+        # Skip first 300 days to get full windows
+        context.i += 1
+        if context.i < 300:
+            return
+
+        # Compute averages
+        # history() has to be called with the same params
+        # from above and returns a pandas dataframe.
+        short_mavg = history(100, '1d', 'price').mean()
+        long_mavg = history(300, '1d', 'price').mean()
+
+        sym = symbol('AAPL')
+
+        # Trading logic
+        if short_mavg[sym] > long_mavg[sym]:
+            # order_target orders as many shares as needed to
+            # achieve the desired number of shares.
+            order_target(sym, 100)
+        elif short_mavg[sym] < long_mavg[sym]:
+            order_target(sym, 0)
+
+        # Save values for later inspection
+        record(AAPL=data[sym].price,
+               short_mavg=short_mavg[sym],
+               long_mavg=long_mavg[sym])
+
+You can then run this algorithm using the Zipline CLI. From the command
+line, run:
+
+.. code:: bash
+
+    python run_algo.py -f dual_moving_average.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
+
+This will download the AAPL price data from Yahoo! Finance in the
+specified time range and stream it through the algorithm and save the
+resulting performance dataframe to dma.pickle which you can then load
+and analyze from within python.
+
+You can find other examples in the zipline/examples directory.
+
+Contributions
+=============
+
+If you would like to contribute, please see our Contribution Requests:
+https://github.com/quantopian/zipline/wiki/Contribution-Requests
+
+.. |Gitter| image:: https://badges.gitter.im/Join%20Chat.svg
+   :target: https://gitter.im/quantopian/zipline?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
+.. |version status| image:: https://img.shields.io/pypi/pyversions/zipline.svg
+   :target: https://pypi.python.org/pypi/zipline
+.. |downloads| image:: https://img.shields.io/pypi/dd/zipline.svg
+   :target: https://pypi.python.org/pypi/zipline
+.. |build status| image:: https://travis-ci.org/quantopian/zipline.png?branch=master
+   :target: https://travis-ci.org/quantopian/zipline
+.. |Coverage Status| image:: https://coveralls.io/repos/quantopian/zipline/badge.png
+   :target: https://coveralls.io/r/quantopian/zipline
+.. |Code quality| image:: https://scrutinizer-ci.com/g/quantopian/zipline/badges/quality-score.png?b=master
+   :target: https://scrutinizer-ci.com/g/quantopian/zipline/